EP 3936224 A4 20220427 - DATA GENERATION DEVICE, DATA GENERATION METHOD, LEARNING DEVICE, AND LEARNING METHOD
Title (en)
DATA GENERATION DEVICE, DATA GENERATION METHOD, LEARNING DEVICE, AND LEARNING METHOD
Title (de)
DATENERZEUGUNGSVORRICHTUNG, DATENERZEUGUNGSVERFAHREN, LERNVORRICHTUNG UND LERNVERFAHREN
Title (fr)
DISPOSITIF DE GÉNÉRATION DE DONNÉES, PROCÉDÉ DE GÉNÉRATION DE DONNÉES, DISPOSITIF D'APPRENTISSAGE ET PROCÉDÉ D'APPRENTISSAGE
Publication
Application
Priority
- JP 2019042363 A 20190308
- JP 2020008589 W 20200302
Abstract (en)
[origin: EP3936224A1] Provided are a data generation device and method, and a learning device and method to enable appropriate setting of conditions for a production process. A data generation device generates a data set consisting of a plurality of pieces of learning data for training a neural network in which a plurality of layers are connected by a plurality of connection weights, the neural network outputting a production result corresponding to a process condition in a case where the process condition is input in a process for producing a product. At this time, assuming that a total number of the connection weights of the neural network is M0, a plurality of the process conditions of 2 × M0 or more are set. In addition, a production result corresponding to each of the plurality of process conditions is acquired, which is derived by producing the product under each of the plurality of process conditions. The plurality of pieces of learning data consisting of the plurality of process conditions and the production result are generated as the data set.
IPC 8 full level
B01J 19/00 (2006.01); G05B 13/02 (2006.01); G06N 3/08 (2006.01); G16C 20/10 (2019.01); G16C 20/70 (2019.01)
CPC (source: EP US)
B01J 19/0033 (2013.01 - EP US); G06F 18/214 (2023.01 - US); G06N 3/04 (2013.01 - US); G06N 3/08 (2013.01 - EP); G16C 20/10 (2019.01 - EP); G16C 20/70 (2019.01 - EP); B01J 2219/00243 (2013.01 - EP)
Citation (search report)
- [I] RENATA FURTUNA ET AL: "Optimization methodology applied to feed-forward artificial neural network parameters", INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, WILEY, NEW YORK, NY, US, vol. 111, no. 3, 15 December 2010 (2010-12-15), pages 539 - 553, XP071304832, ISSN: 0020-7608, DOI: 10.1002/QUA.22423
- [T] YUNTIAN CHENA ET AL: "Ensemble Neural Networks (ENN): A gradient-free stochastic method", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 3 August 2019 (2019-08-03), XP081455328
- See references of WO 2020184240A1
Designated contracting state (EPC)
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
Designated extension state (EPC)
BA ME
DOCDB simple family (publication)
EP 3936224 A1 20220112; EP 3936224 A4 20220427; CN 113543874 A 20211022; CN 113543874 B 20230630; JP 7098821 B2 20220711; JP WO2020184240 A1 20211202; US 2021390369 A1 20211216; WO 2020184240 A1 20200917
DOCDB simple family (application)
EP 20769853 A 20200302; CN 202080019523 A 20200302; JP 2020008589 W 20200302; JP 2021504931 A 20200302; US 202117407770 A 20210820